As we will soon see, such a framework can be regarded as a generalization of several existing summarization methods.

we list below some possible future extensions: 1) integrating different selection strategies, e.g., the listwise strategy that defines the loss function on all the sentences associated with a document to be summarized, into this framework, 2) exploring different modeling approaches for this framework, 3) investigating discriminative training criteria for training the component models in this framework, and 4) extending and applying the proposed framework to multidocument summarization tasks.


The corpus aims to be maximally useful, and minimally parochial.

this does not limit the options of community members who desire richer formats, since they are free to invest the effort in enriching the existing data.


These correspondences are just aligned text fragments, albeit much smaller than a sentence.

indeed, the amount of work involved is one motivation for the lengths to which we have gone to keep the data format simple.


This value was chosen in a small pre-test.

two signatures of words in different languages cannot be directly compared; we compare them using a lexicon L as explained below.


For example, TextRunner uses a small set of handwritten rules to heuristically label training examples from sentences in the Penn Treebank.

we use the same matching sentence set behind DB p to generate positive examples for wOE pos .


This is because long sentences tend to have complicated and long-distance relations which are difficult for shallow features to capture.

the idea is to elimi-nate distinctions which are irrelevant for recognizing (domain-independent) relations.


WOE parse dramatically improves performance, especially on recall.

we compute the precision & recall based on all extractions, while Banko et al.


Fortunately, DBpedia (Auer and Lehmann, 2007) provides a cleaned set of infoboxes from 1,027,744 articles.

in total, WOE builds a database (named DB p ) of 15,333 distinct patterns and each pattern p is associated with a frequency -the number of matching sentences containing p. 185 patterns have f p ≥ 100 and 1929 patterns have f p ≥ 5.


Character-Level Regular Expression CPSL cannot specify character-level regular expressions that span multiple tokens.

this rigid matching priority can lead to mistakes.


In the absence of an escape to arbitrary procedural code, AQL is strictly more expressive than a CPSL grammar.

no Caps annotations are output by phase P 1 .


We compare SystemT's approach against cascading grammars, both theoretically and with a thorough experimental evaluation.

systemT places no explicit constraints on the order of rule evaluation, nor does it require that intermediate results of an annotator collapse to a fixed-size sequence.


Bakhtin's term for this causal relationship was chronotope: the "intrinsic interconnectedness of temporal and spatial relationships that are artistically expressed in literature," in which "space becomes charged and responsive to movements of time, plot, and history" (Bakhtin, 1981, 84).

it is commonly held that the novel is a literary form which tries to produce an accurate representation of the social world.


Another way to interpret hypothesis #1 is that social networks with more characters tend to break apart and be less connected.

in discussion forums, people tend to use addresses such as "Hi Tom," while in novels, a system must determine both the speaker of a quotation and then the intended recipient of the dialogue act.


Table 1: A sampling of learned weights for the lexical zero feature.

we learn a separate weight for each feature for each part-of-speech tag in our data.


Table 6 summarizes the results per response category for the WordNet-based approach (which can thus be compared to the category I results in table 5).

the negation can be present in the main clause when the adjectival predication is embedded, as in example (7).


We propose the Importance-Driven Turn-Bidding (IDTB) model where conversants bid for the turn based on the importance of their utterance.

the mean policy cost between the turn-taking approaches and user conditions are shown in table 3.


In our domain, performance is measured by dialogue length and solution quality.

.. For this assumption to be met, care is required when deciding which features to include for learning.


They employ a semantic clustering model to relate entities.

dipper and Zinsmeister (2009) distinguish multiple uses of the VF depending on whether it contains an element related to the surrounding discourse.


Again, the literature is split into syntactic and semantic models.

firstly, the raw reading times tend to have a skew distribution and taking logs produces something closer to normal, which is preferable for modeling.


Data The models discussed in the previous section were evaluated against an eye-tracking corpus.

iCD assumes that words carry prior semantic expectations which are updated upon seeing the next word.


Furthermore, word frequency and trigram probability are highly correlated.

the simpler space is based on word cooccurrence counts; it constructs the vector representing a given target word, t, by identifying all the tokens of t in a corpus and recording the counts of context words, c i (within a specific window).


In our analyses, we treat participant as a random factor, which means that our models contain an intercept term for each participant, representing the individual differences in the rates at which they read.

log(t) = ∑ i β i x i implies t = ∏ i e β i x i .


The grammar engineering process we describe, which we dub rebanking, is intended to reduce this gap, tightening the feedback loop between formal and computational linguistics.

we did not change any of the parser's maximum entropy features or hyperparameters, which are tuned for CCGbank.


• For each Wikipage w whose lemma is monosemous both in Wikipedia and WordNet we map w to its only WordNet sense.

while the contribution of any individual user might be imprecise or inaccurate, the continual intervention of expert contributors in all domains results in a resource of the highest quality (Giles, 2005).


A Wikipedia page (henceforth, Wikipage) presents the knowledge about a specific concept (e.g.

we plan in the near future to apply BabelNet to the challenging task of cross-lingual wSD (Lefever and Hoste, 2009).


Nonetheless, the results are generally high, with a peak for French, since WOLF has been created semi-automatically by combining several resources, including Wikipedia.

that is we calculate the ratio of word senses in our gold-standard resource F that also occur in the corresponding synset S B to the overall number of senses in F. our gold-standard resources cover only a portion of the English WordNet, whereas the overall coverage of BabelNet is much higher.


Their work relied on a supervised parser and a rule-based argument identification (both during training and testing).

the average number of words per argument is 1.6 (1.7).


Since most non-prepositional arguments are cores, we focused on prepositional arguments, which are roughly equally divided between cores and adjuncts.

in "The troops are based [in the park]", the same argument is obligatory, since being based requires a place to be based in.


This property of the core-adjunct distinction greatly simplifies the task for supervised algorithms (see Section 2).

the common evaluation focuses on the quality of the allowable frames acquired for each verb type, and not on the classification of specific arguments in a given corpus.


This is mainly due to the coherence between the distributions of lexical and grammatical phenomena in the test and training data.

syntax does not entirely characterize argument semantics.


For a given word, w i , we sort the contexts in descending order according to c(w i , f k ) and retrieve the top-L contexts.

in experiments, we estimate semantic similarities using a large amount of Web data in Japanese and show that the proposed measure gives better word similarities than a non-Bayesian Bhattacharyya coefficient or other well-known similarity measures such as Jensen-Shannon divergence and the cosine with PMi weights.


Our method is the simplest one as a Bayesian method.

we also included the results for the case where L = M = 3600 and N = 2, 000.


In-ternet forums, blogs, wikis, podcasts, instant messaging, and social networking.

considering the semantic similarity between nodes, we use another variant of the PageRank algorithm to calculate the weight of comment : where is a damping factor, and , is the normalized weight of comment referring to defined as where , is an entry in the graph adjacency matrix M and is a constant to avoid division by zero.


In this work, we use a graph model to differentiate the importance of each comment.

this allows us to measure the degrees of generalization and specialization of two articles.


Inten-tion and concerns of active users may change as the discussion goes on.

the second one is the Blog data set containing 15 blog articles with user comments and 15,110 articles obtained from the Myhome Web site 2 .


The speller systems we developed in this study are evaluated using the following three metrics.

the lexicon is stored using a trie-based data structure that allows efficient search for all terms within a maximum edit distance.


A query reformulation session contains a list of URLs that record user behaviors that relate to the query reformulation functions, provided by a Web search engine.

recent research on query spelling correction has focused on exploiting noisy Web data and query logs to infer knowledge about misspellings and word usage in search queries.


The goal of our research is to create semantic class taggers that can assign a semantic class label to every noun phrase in a sentence.

the contextual classifier then labels new instances, to expand and diversify the training set.


First, we generate training instances by automatically labeling each instance of a seed word with its designated semantic class.

in future work, we plan to investigate whether these semantic taggers can be used to improve other tasks.


Analysis of veterinary data could not only improve pet health care, but also provide early warning signs of infectious disease outbreaks, emerging zoonotic diseases, exposures to environmental toxins, and contamination in the food chain.

we only apply this heuristic to instances that are labeled with extremely high confidence (θ cf ≥ 2.5) and that pass a global sanity check, gsc(w) ≥ 0.2, which ensures that a relatively high proportion of labeled instances with the same head noun have been assigned to the same semantic class.


We begin using word features and obtain a token-level F1score of .311 for text and .311 for numeric attributes.

if the labeled subsequences are long and have substructure, e.g., 'San Remo, Italy', our system splits at the separator token, and creates additional seed sets from prefixes and postfixes.


The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ARO, DARPA, NSF, ONR, or the United States Government.

the USP system (Poon and Domingos, 2009) can extract formulas and appears to be fairly robust to noise.


The best way to capture such commonalities is by forming an ISA hierarchy among the clusters.

the first formula is the core of the model and represents the mixture of property values given the cluster.


USP was able to resolve common syntactic differences such as active vs. passive voice.

these priors and cut-off values can be tuned to control the granularity of the induced ontology and clusters.


The experimental results suggest that by combining iBTK and dBTK together, we can achieve more improvement.

given feature spaces defined in the last two sections, we propose a 2-phase sub-tree alignment model as follows: In the 1 st phase, a kernel based classifier, SVM in our study, is employed to classify each candidate sub-tree pair as aligned or unaligned.


Syntax based Statistical Machine Translation (SMT) systems allow the translation process to be more grammatically performed, which provides decent reordering capability.

other than the factor of the amount of training data, this is also because the plain features in Table 3 are not as effective  as those in Table 4, since they are trained on  FBIS corpus which facilitates Table 4 more with respect to the domains.


This suggests that the structural features captured by BTKs are quite useful for the sub-tree alignment task.

the grammatical tags and syntactic tree structures are more accurate in HIT corpus, which facilitates the performance of BTKs in Table 3.


To address those deficiencies, Tinsley et al.

we propose the dependent Bilingual Tree kernel (dBTK) to jointly evaluate the similarity across sub-tree pairs by enlarging the feature space to the Cartesian product of the two substructure sets.


First of all, it imposes a 1-to-1 constraint in word alignment.

in fact there have been various attempts in relaxing the 1-to-1 constraint.


1 Alternatively it can be defined as the search of the minimal number of E-spans per F-span.

6) Boolean features like whether a word in the word pair is a stop word.


When extended to supervised/discriminative framework, ITG runs even more slowly.

the interval boundaries are the intersections between the correct E-span and all other candidate E-spans (as shown in Figure 4(b)).


The decoding time (seconds per sentence) of tree-to-string translation is listed as well.

the three nodes are not included in one (minimal) translation rule.


The string-to-tree decoder searches for the optimal derivation d * that parses a Japanese string F into a packed forest of the set of all possible derivations D: This formula differs from Equation 2 by replacing E t with F in s(d|•) and adding g (d), which is the number of glue rules used in d. Further definitions of s(d|F ) and f (r) are identical with those used in Equation 2.

we use a CKY-style algorithm with beam-pruning and cube-pruning (Chiang, 2007) to decode Japanese sentences.


Despite the fact that there is a strongly context-free CCG which generates all of the derivations in CCGbank, it is still possible that the grammar learned by the Clark and Curran parser is not a context-free grammar.

these rule systems vary from a small rule set, motivated theoretically (Vijay-Shanker and Weir, 1994), to a larger rule set, motivated linguistically, (Steedman, 2000) to a very large rule set, motivated by practical coverage (Hockenmaier and Steedman, 2007;Clark and Curran, 2007).


As Table 6 shows, this change translates into an improvement of up to 0.75% in F-score on Section   Tables 2, 4 and 6, are similar for all of the training algorithms.

in all cases, the new models have lower tagging ambiguity, and lower supertag accuracy.


When we use Sassano's algorithm, we have to generate training examples for the classifier by using the algorithm in Figure 4.

one of most promising approaches to this issue is active learning.


for this reason, we have extended the model which led to PROMODES-H, a higher-order probabilistic model.


The goal was to mimic each algorithm since ParaMor is rule-based and there is no access to Morfessor's internally used probabilities.

can we pinpoint the exact contribution of each individual algorithm to the improved result?


(Alternately, they could keep the soft clustering, with the representation for a particular word token being the posterior probability distribution over the states.)

(2009), they extend their semi-supervised approach to more general conditional models.)


Joseph Turian and Yoshua Bengio acknowledge the following agencies for research funding and computing support: NSERC, RQCHP, CIFAR.

the one-hot representation of a word suffers from data sparsity: Namely, for words that are rare in the labeled training data, their corresponding model parameters will be poorly estimated.


Then, a binary text classifier is trained using the textual representation and applied to new words.

the proposed method is faster and does not need a large corpus.


They achieve a very impressive improvement across five domains.

also, we find that our performances are similar to the ones (described as fully supervised results) reported in Dasgupta and Ng (2009) where the same data in the four domains are used and 10-fold cross validation is performed.


In fact, we randomly select 50 documents in the domain of kitchen appliances and find that 80% of the documents take both personal and impersonal sentences in which both of them express explicit opinions.

the effectiveness of different sizes of chosen samples in each iteration is also evaluated like 1 2 3 6 n n n = = = and 1 2 3 3, 6 n n n = = = (this assignment is considered because the personal view classifier performs worse than the other two classifiers).


Kennedy and Inkpen (2006) use a term-counting method with a set of seed words to determine the sentiment.

we apply the ME classification algorithm for further combination and co-training.


These dependency paths were then matched against each pair in the DIRT database, and all pairs of associated relations were collected producing about 26,000 inference rules.

the key difference in JointLDA (versus LDA) is that instead of one, it maintains two sets of topics (latent distributions over words) denoted by β and γ, one for classes of each argument.


LinkLDA, however, is more flexible; rather than requiring both arguments to be generated from one of |Z| possible pairs of multinomials (β z , γ z ), Lin-kLDA allows the arguments of a given extraction to be generated from |Z| 2 possible pairs.

class-based approaches, first proposed by Resnik (1996), are the most studied of the four.


We first compare the three LDA-based approaches to each other and two state of the art similarity based systems (Erk, 2007) (using mutual information and Jaccard similarity respectively).

lDA-SP is able to capture information about the pairs of topics that commonly co-occur.


However, it is a simple baseline to implement with these freely available counts.

car/rock), and asks a model to identify the original.


The sentence representations in the middle part of Figure 2 show these sentences with some of the dependency relations (relevant to our transformations) extracted by the parser, explicitly marked as labeled links.

phrase extraction algorithms can conflate all constructs like in their .


For transliteration options that are TM-unknown, the p w (h, u) and p w (u) factors becomes zero and the translation model probability as given by equation (13) becomes: In such cases the λ factor cancels out and no weighting of word translation vs. transliteration  Table 6: Applying Heuristic 3 and its Combinations with other Heuristics to Model-2 occurs anymore.

(2008) use a tagger to identify good candidates for transliteration (which are mostly NEs) in input text and add transliterations to the SMT phrase table dynamically such that they can directly compete with translations during decoding.


The options are further re-ranked based on different measures such as web counts and using coreference to resolve ambiguity.

of this, transliterations are sometimes incorrectly favored over their translation alternatives.


Previous attempts have dealt with the overfitting problem by limiting the maximum phrase length (DeNero et al., 2006;Marcu and Wong, 2002) and by smoothing the phrase probabilities by lexical models on the phrase level (Ferrer and Juan, 2009).

the produced phrase alignment can be given as a single best alignment, as the n-best alignments or as an alignment graph representing all alignments considered by the decoder.


Ideally, we would produce all possible segmentations and alignments during training.

we can approximate the space of all possible hypotheses by the search space that was used for the alignment.


Even if bigram features are dependent on x, it is still possible to compute better bounds if several features are mutually exclusive, as discussed in (Esposito and Radicioni, 2009).

most of the exact algorithms, including ours, do not require such a manual effort.


The combinatory rules in B&K-CCG, given in Figure 4, are defined to be sensitive to the slash types.

consider the following instance of forward harmonic composition: This rule should be understood as obtaining its conclusion a=c from the primary premise a=b by the removal of the argument =b and the subsequent transfer of the argument =c from the secondary premise.


A second approach concerns the precise definition of the generalized composition rules, about which there is a surprising degree of disagreement.

slashes with the types˘and can only be eliminated by harmonic and crossed compositions, respectively.


The output of the ranker is always a real valued score, so a global rank order is always obtained.

while for system-level predictions the meta ranker was only useful for grammaticality, at the input level it outperforms every individual feature class for each of the five questions, obtaining accuracies around 70%.


Further, both sets of experiments perform evaluations of mixed sets of human-produced and machine-produced summaries, so the results may be influenced by the ease of discriminating between a human and machine written summary.

for each of these questions, all summaries were manually rated on a scale from 1 to 5, in which 5 is the best.


To identify these potential problems, we adapt the features for surface cohesive devices to indicate whether referring expressions and discourse connectives appear in the summary with the same context as in the input documents.

we used the two features introduced by Soricut and Marcu (2006).


This feature can affect the belief of the corresponding hidden node, which can in turn affect its neighbors' beliefs.

table 8 shows a portion of the survey generated from the QA context corpus.


• King gave his "I have a dream" speech in 1963.

we used the same model (trained on the CNN corpus) to generate summaries for the DUC-2002 corpus 2 .


There has been a huge interest in the automatic identification and extraction of opinions from free text in recent years.

the is-Reference attribute labels polar targets which are anaphoric references.


To ease the annotation process, the holder is not labeled when this is the author.

explicit expressions of opinions typically contain specific cues, i.e.


Here, we use the extracted syntantic clues to identify words that are likely to serve as specific targets for opinions about the topic in the relevant documents.

among documents assessed as opionionated by TREC assessors, only 13% did not contain matches of the lexicon entries, compared to 27% of non-opinionated documents, which does indicate that our lexicon does attempt to separate non-opinionated documents from opinionated.


* 삼성(Samsung) 휴대전화(cell phone) 배터리는 (battery) 그럭저럭(somehow or other) 오래간다(last long).

our multilingual subjectivity analysis systems were evaluated on the test corpora described in Section 5.1 (Table 3).


This Naive Bayes classifier builds upon a corpus annotated by a high-precision classifier with the bootstrapping of the corpus and extraction patterns.

we measure the correlations between the sums of P, the sums of R, and the sums of F to κ and ρ for all pairs of systems.


These endeavors have been successful in constructing lexicons, annotated corpora, and tools for subjectivity analysis in multiple languages.

we cannot always expect a highprecision multilingual subjectivity classifier to be multilanguage-comparable as well.


1 Most previous approaches make decisions based on a pretuned classification threshold τ as follows where Φ is a classifier or a confidence measure and θ is the parameter set of Φ.

the precision P re is the percentage of words correctly tagged as translation errors.


The A-Fra and B-Fra entries below are produced by our Eng-Fra TrustRank-MT system, starting from A-Eng and B-Eng 3 , respectively.


Such comparisons are done either at lexical level (Papineni et al., 2002;Doddington, 2002), or at linguisticallyricher levels using paraphrases (Zhou et al., 2006;Kauchak and Barzilay, 2006), WordNet (Lavie and Agarwal, 2007), or syntax (Liu and Gildea, 2005;Owczarzak et al., 2007;Yang et al., 2008;Amigó et al., 2009).

we also report the BLEU performance of the TrustRank-MT system on the Regression Test set.


Similar with the conclusion for English-Spanish, the regression performance is currently too poor to allow us to confidently use the absolute document-level predicted BLEU numbers as indicators of translation accuracy.

solutions that require training labels produced manually by humans (Gamon et al., 2005;Albrecht and Hwa, 2007) have difficulties producing prediction models fast enough, trained on enough data, and customized for specific domains.


Ideally our aim is to obtain a level of 0.9 precision at the cost of some recall, if necessary.

the SVMtraining and testing is performed on the remaining 8K sentences with 4-fold cross validation.


In classical settings involving SVMs, confidence levels are represented as margins of binary predictions.

we compute this score for both the MT output and the TM hit.


There is no overlap between the training-sets, the development-set and the testing-set.

in more detail ， for transliteration, the model adopted in (Huang et al., 2003), which first Romanizes Chinese characters and then transliterates them into English characters, is used for .


In contrast, English NE boundaries are easier to identify due to capitalization clues.

for translation, conditional probability is directly used for .


Similar to what we have done on the bilingual alignment factor above, Exp9 (named Exp6+N-Bigram) adds the normalized NEtype dependant bigram to Exp6 for removing the bias induced by having different NE lengths.

only the English NE recognizer and the alignment model are updated during training iterations.


First, given sentence s in document d, we sample a value for z d,s given the values of all other z and y variables using the following formula: In the formula above, z ¬{d,s} is the current aspect assignment of all sentences excluding the current sentence.


To cluster sentences, we could have used a straightforward solution similar to document clustering, where sentences are represented as feature vectors using the vector space model, and a standard clustering algorithm such as K-means can be applied to group sentences together.

we propose a method that can overcome these two limitations.


Finally words such as "Modena" and "Chicago" are specifically associated with the subject enti-ties being discussed, that is, they are specific to the summary documents.

we do not prune any pattern except to merge small patterns with the large ones that contain them.


To mine comparators from comparative questions, we first have to detect whether a question is comparative or not.

it sig-nificantly improves recall in both tasks while maintains high precision.


For question processing, we used our own statistical POS tagger developed in-house 4 .

the recall is significantly lower than ours.


The majority class baselines produce the same results as for English.

it is hard to (eClass = OCCURRENCE) and ( eTense = INF) and ( ePolarity = POS) => lRelType= AFTER (183.0/77.0) ( eTense = FI) => lRelType= AFTER (55.0/10.0) (eClass = OCCURRENCE) and ( eTense = IR-PI+INF) => lRelType= AFTER (26.0/4.0) (eClass = OCCURRENCE) and ( eTense = PC) => lRelType= AFTER (15.0/3.0) (eClass = OCCURRENCE) and ( eTense = C) => lRelType= AFTER (17.0/2.0) ( eTense = PI) => lRelType= OVERLAP (388.0/95.0) (eClass = ASPECTUAL) and ( eTense = PC) => lRelType= OVERLAP (9.0/2.0) => lRelType= BEFORE (1863.0/373.0) Here, oAdjacent is the ORDER-adjacent attribute.


Of the taxonomies presented by purely linguistic studies, our categories are most similar to those proposed by Warren (1978), whose categories (e.g., MATERIAL+ARTEFACT, OBJ+PART) are generally less ambiguous than Levi's.

several lines of work, including that of Barker and szpakowicz (1998), use memory-based methods.


This idea is a modification of the selectional preference view of Wilks.

birke and Sarkar (2006) present a sentence clustering approach for non-literal language recognition implemented in the TroFi system (Trope Finder).


For example, considering Example (5), Figure 2 illustrates the representation of the structured feature for this relation instance.

and thus an SVM classifier can be learned and then used for recognition.


For example, in (4), we have an improper relation between skall ('shall') and sambeskattas ('be taxed together'), as in figure 3.


in joint decoding of member systems, the search space is structured as a translation hypergraph where the member systems can share their translation hypotheses.


To convert it into a distance measure, we can invert it h(a, b) plsk = 1/s(a, b) plsk .

in addition, the methods they used often provide conflicting results on which trees are considered as balanced (Shao and Sokal, 1990).


After converting the similarities to distance, we plug the distance matrix into our structural SVM.

we are also interested in structural accuracy (S-Acc) to see whether the structural SVMs make fewer "big" mistakes.


For a given event, we re-tag the entity mentions that have not already been assigned as arguments of that event by the confident-event or conflict table.

these rules basically serve to adjust trigger and argument classification to achieve document-wide consistency.


Compared to the within-event-type rules, the cross-event model yields much more improvement for trigger classification: rule-based propagation gains 1.7% improvement while the cross-event model achieves a further 7.3% improvement.

5 To use document-level information, we need to collect information based on the sentence-level baseline system.


We will not use information in the conflict table to infer the event type or argument/roles for other event mentions, because we cannot confidently resolve the conflict.

automatic Content Extraction (aCE) defines an event as a specific occurrence involving participants 1 , and it annotates 8 types and 33 subtypes of events.


When designing the interview and navigation tasks that were to be carried out during the data collection, we focused on designing them so that the participants were encouraged to discuss as much as possible with each other.

declarative phrases are most common, followed by incomplete, interrogative (for interview topics) and noun phrases.


IDIS determines workload based on the driver's behaviour (for example, steering wheel movements or applying the brake).

the dialogue system should consider the cognitive load of the driver and adjust the dialogue accordingly.


We build a regression model using sentence scores as output and selected salient features as input variables described below: We compile our training dataset using sentences from different document clusters, which do not necessarily share vocabularies.

(III) Other Features (OF): Term frequency of sentences such as SUMBASIC are proven to be good predictors in sentence scoring (Nenkova and Vanderwende, 2005).


Similarly, sim 2 provides information on the similarity between the weights of the topics in each sentence.

the similarity of a candidate sentence o m to a summary sentence s n sharing the same path is a measure of strength, indicating how likely o m is to be included in the generated summary (Algorithm 1): Let c om be the path for a given o m .


Our work is different from all the above approaches in that we attempt to discriminate among hierarchical rules based on: 1) the degree of bilingual semantic similarity between source and target translation units; and 2) the monolingual semantic similarity between occurrences of source or target units as part of the given rule, and in general.

our aim in this paper is to characterize the semantic similarity of bilingual hierarchical rules.


However, the two vectors in Equation (2) cannot be directly compared because the axes of their spaces represent different words in different languages, and also their dimensions I and J are not assured to be the same.

this work attempts to measure directly the sense similarity for units from different languages by comparing their contexts 1 .


In another words, WSD explicitly tries to choose a translation given the current source context, while our work rates rule pairs independent of the current context.

all the allowed bilingual corpora except the UN corpus and Hong Kong Hansard corpus have been used for estimating the translation model.


In Figure 1, we have distinctness assertions between nodes in two densely connected clusters that are tied together only by a single spurious link.

a number of projects have used Wikipedia as a database of named entities (Ponzetto and Strube, 2007;Silberer et al., 2008).


We call the method the Highlighted N-Gram method, or HNG, for short.

one might think then that this would be a great sentence to have translated because the machine can potentially learn a lot from the translation.


VocabGrowth (hereafter VG) selection is modeled after the best methods from previous work , which are based on preferring sentences that contain phrases that occur frequently in unlabeled data and infrequently in the so-far labeled data.

the problem of automatically detecting when to stop AL is a substantial one, discussed at length in the literature (e.g., (Bloodgood and Vijay-Shanker, 2009a;Schohn and Cohn, 2000;Vlachos, 2008)).


This reflects the fact that the k-Piecewise Testable languages are the boolean closure of the Strictly k-Piecewise languages.

angluin (1980b) provides necessary and sufficient properties of language classes which are identifiable in the limit from positive data.


A learner is a function φ which maps initial finite sequences of texts to grammars, i.e.

f ⋄ : A → P(B) (note f ⋄ is not surjective).


a Note that this is indeed a bijection and that for w 1 , w 2 ∈ Σ * , we have Now, we define M as follows: Using the above fact about # and a simple induction on m, we find that Evaluating the two acceptance conditions, we find them satisfied exactly if # .

the convolution product can be seen as a condensed version of the tensor product.


The training time is proportional to the number of GA iterations multiplied by the number of individuals in a population times the fitness evaluation (ROUGE) time.

in the absence of such techniques, the only alternative to language-independent summarization would be the labor-intensive translation of the entire document into a common language.


Crossover is performed under the assumption that new solutions can be improved by re-using the good parts of old solutions.

methods that require pre-defined threshold values are marked with a cross and listed in Table 2 together with the average threshold values obtained after method evaluation on English and Hebrew corpora.


All elements are generated from a standard normal distribution, with µ = 0 and σ 2 = 1, and normalized to sum up to 1.

mUSE total computation time, given a trained model, is also linear (at factor of the number of metrics in a combination).


To combat it, we proposed a simple iterative inference algorithm.

measuring how well the model predicts the alignment between the text and the observable records describing the entire world state.


One natural approach would be to use a form of belief propagation (Pearl, 1982;Murphy et al., 1999), where messages pass information about likely semantics between the texts.

the problem of producing multiple sequence alignment, especially in the context of sentence alignments, has been extensively studied in NLP (Barzilay and Lee, 2003).


(2008) use deep learning techniques based on semi-supervised embeddings to improve an SRL system, though their tests are on in-domain data.

the Multi-Span-HMM model performs near the state-of-the-art on the WSJ test set, and its F1 on out-of-domain data drops only about half as much as comparable systems.


Combined with the fact that 93% of all individual event descriptions occurred only once, this makes it challenging to align the different ESDs with each other.

they are typically short and elliptic (no overt subject), they lack determiners and use infinitive or present progressive form for the main verb.


Some clusters contain nouns, which are candidate arguments; others contain verbs, which take arguments.

we achieve the different occurrence coverage numbers of figure 3 by using the most frequent N words from the list that give the specific coverage 6 .


In what follows, we compare the performance of the BabySRL across the four parsers.

verbPos features did not help the SRL assign different roles to the subjects of simple transitive and intransitive sentences: 'Mommy' in 'Mommy krads you' and 'Mommy krads' are both represented simply as pre-verbal.


Furthermore, because such models are both specific to the number of participants and to the order in which participant states are concatenated together, it is generally intractable to train them on material from other conversations.

the depicted perplexities are not unweighted averages over the whole meeting of duration t as in Equation 8, but over a 60second Hamming window centered on each t. the first trajectory, the dashed black line, is obtained when the entire meeting is used to estimate Θ CD , and is then scored by that same model (an "oracle" condition).


Experiments show that direct multi-participant models do not generalize to held out data, and likely never will, for practical reasons.

substantial research effort has been invested in recent decades into the computational study and automatic processing of multi-party conversation.


Higher values of ρ would lead to more , leading to more overlap than observed in this work.

in the easier mismatched D+C condition, it is outperformed by the CI model due to behavior differences among participants, which the EDO model small groups and large groups, represented in their study by K = 5 and K = 10, and noted that there is a smooth transition between the two extremes; this provides some scope for interpolating small-and large-group models, and the EDO framework makes this possible.


This reply type indicates that the system failed to present the information the user was looking for.

4 this NLG predicted user act does not actually change the overall dialogue state (e.g.


(In Model 5, stable variation very near {1,1} or {2,2} is possible.)

), and are the subject of much recent work (reviewed by Hansson, 2008) For English N/V pairs, M. Kelly and collaborators have shown mistransmission effects which they propose are responsible for the directionality of the most common type of N/V pair stress shifts ({1,1}, {2,2}→{1,2}), based on "rhythmic context" (Kelly, 1988;Kelly and Bock, 1988;Kelly, 1989).


Survival dictates, for each cognate group, which languages have words in that group.

in this paper, each ϕ takes the form of a parameterized edit distance similar to the standard Levenshtein distance.


In practice, for the automata generated in our model, minimization removes at most half the states, which is not sufficient to counteract the exponential growth.

in our setting, however, n-best lists are problematic; early experiments showed that a 10,000-best list for a typical message only accounts for 50% of message log perplexity.


For instance, Spanish /nombRar/ "name" is grouped together with Portuguese /num1RaR/ "number" and Italian /numerare/ "number," largely because the rule Portuguese /u/ → Spanish /o/ is applied outside of its normal context.

for any fixed w a : where align(w a , w d ) is the set of possible alignments between the phonemes in words w a and w d .


Enqueued Expanded Zeros Accuracy Word Error Rate  1  2:03:06  964  964  44157  100%  0%  2  0:13:00  132  132  5197  100%  0%  3  0:05:42  91  91  3080  100%  0%  4 Unfinished  As far as the running time of the algorithm goes, we see a substantial variance: from a few minutes to several hours for most of the longer ciphers, and that there are some that take longer than the threshold we gave in the experiment.

every possible full solution to a cipher C will produce a plaintext string with some associated language model probability, and we will consider the best possible solution to be the one that gives the highest probability.


It is easy to extend this kind of cipher so that the plaintext alphabet is different from the ciphertext alphabet, but still stands in a one to one correspondence to it.

there is substantial variability in the the running times seen.


As we will see, there are various strategies for approaching this problem.

a deleting rule such as q x 2 ) necessitates the introduction of a new nonterminal ⊥ that can generate all of T Σ with weight 1.


A STAG tree transformation can also be computed by an STSG using explicit substitution.

another effect of this restriction is that the number of operable nodes (i.e., the nodes to which an adjunction must still be applied) is known at any given time.


We prove that any tree transformation computed by an STAG can also be computed by an STSG using explicit substitution.

, t k are the term representations of its k children.


An extended tree transducer (for short: XTT) 6 is a system M = (Q, Σ, ∆, I, R) where • Q is a finite set of states, • Σ and ∆ are alphabets of input and output symbols, respectively, • I ⊆ Q is a set of initial states, and • R is a finite set of rules of the form  where we usually • add the state q as root node of the left-hand side 7 , and • add the states q 1 , .

many standard algorithms for STAG can easily be derived from the corresponding algorithms for STSG.


that it is not restricted to LR (a special case of shift-reduce), and thus does not require building an LR table, which is impractical for modern grammars with a large number of rules or features.

where ℓ is the step, c is the cost, and the shift cost ξ and reduce costs λ and ρ are: Figure 1: Deductive system of vanilla shift-reduce.


we make the following contributions: • theoretically, we show that for a large class of modern shift-reduce parsers, dynamic programming is in fact possible and runs in polynomial time as long as the feature functions are bounded and monotonic (which almost always holds in practice); • practically, dynamic programming is up to five times faster (with the same accuracy) as conventional beam-search on top of a stateof-the-art shift-reduce dependency parser; • as a by-product, dynamic programming can output a forest encoding exponentially many trees, out of which we can draw better and longer k-best lists than beam search can; • finally, better and faster search also leads to better and faster learning.


In TiGer, sentences whose main verbs are formed from auxiliary-participle combinations, are annotated by embedding the participle under an extra VP node and non-subject arguments are sisters to the participle.

the considerable drop in quality between gold trees and parser output clearly shows that a good parse tree is an important prerequisite for reasonable function labelling.


The previously described implicit grammar G I defines a posterior distribution P (d I |s) over a sentence s via a large, indexed PCFG.

of Indexed Symbols Word-level Trees  1,900,056  Word-level Graph  903,056  Character-level Trees  12,280,848  Character-level Graph  1,109,399   Table 2: Number of indexed symbols for word-level and character-level parsing and their graph versions (for allfragments grammar with parent annotation and one level of markovization).


Here, the parameter α is a balance factor indicating the importance of the semantic parsing model.

table 4 shows that integrated parsing benefits syntactic and semantic parsing when automatic word segmentation is considered.


Ideally, we should perform SRL on as many parse trees as possible, so as to enlarge the search scope.

in addition, it is found that 11% of predicates in a sentence are speculatively attached with two or more core arguments with the same label due to semantic parsing errors (partly caused by syntactic parsing errors in automatic parse trees).


For example, "北/north/B_NR", "京/capital/M_NR", and "市/city/E_NR" will be merged as "北京市 /Beijing/NR", "是/is/S_VC" will become "是 /is/VC".

if no constituent in the parse tree can map the same text span to an argument in the manual annotation, the system will not get a correct annotation.


The pipeline parsing approach employed in this paper is largely motivated by the general framework of re-ranking, as proposed in Sutton and McCallum (2005).

taking those expected semantic roles into account would help the syntactic parser.


The results are convincing; they demonstrate both the robustness and the competitiveness of the presented ideas.

first, SCL identifies features that generalize across domains, which the authors call pivots.


Recent work in cross-language text classification focuses on the use of automatic machine translation technology.

the learning rate schedule from PEGASOS is adopted (Shalev-Shwartz et al., 2007), and the modified Huber loss, introduced by Zhang (2004), is chosen as loss function L. 3 SGD receives two hyperparameters as input: the number of iterations T , and the regularization parameter λ.


In general, the problem of cross-lingual topic extraction can be defined as to extract a set of common cross-lingual latent topics covered in text collections in different natural languages.

we propose a novel probabilistic topic model (i.e.


Some previous work on multilingual topic models assume documents in multiple languages are aligned either at the document level, sentence level or by time stamps (Mimno et al., 2009;Zhao and Xing, 2006;Kim and Khudanpur, 2004;Ni et al., 2009;Wang et al., 2007).

chinese to English, we look up the bilingual dictionary and do word-to-word translation.


The test sets were POS-tagged and lemmatized using RASP (Briscoe and Carroll, 2006).

the assumption is that the query is generated by a collection of conditionally independent words (Song and Croft, 1999).


The implementation was the same as in Sim.

we present a new rational model of eye movement control in reading, the central assumption of which is that eye movement decisions are made to obtain noisy visual information as the reader performs Bayesian inference on the identities of the words in the sentence.


At its core, the framework we are proposing is one of reading as Bayesian inference.

the optimal values of α and β for each γ ∈ {.025, .1, .4} are given in table 1 along with the mean values for L and t found at those parameter values.


's result on unambiguous syntactic structures and we present a new experiment on involving a garden path which was designed to be similar to the Grodner et al.

this is also true if we limit ourselves to syntactic probabilities (which are the probabilities listed in Figure 4).


We note that Surprisal does indeed show processing difficulty on the word 'by' in the garden path experiment.

so far, probabilistic models of sentence processing have been largely limited to syntactic factors.


Embedding difference weighs the probabilities with center-embedding depths and then normalizes the values; since the measure is a weighted average of embedding depths rather than a probability distribution, µ EMB is not always less than 1 and the correspondence with Kullback-Leibler divergence (Levy, 2008) does not hold, so it does not make sense to take the logs.

fitting the best model by AIC would actually prune away some of the factors as relatively insignificant, but these smaller models largely accord with the significance values in the table and are therefore not presented.


We illustrate the process using the example in figure 1.

we believe that if a system could recognize key negation phenomena such as Named Entity mismatch, presence of Excluding arguments, etc.


We write B r (x, y) for a bridging relation, where r is its type.

almost a quarter of the focus terms were verbal phrases.


Propositions have been mostly presented through unstructured textual summaries or manually-constructed ontologies, which are expensive to build.

presenting information according to this subgraph distinguishes between propositions dealing with headache treatments and propositions dealing with headache risk groups.


We used Integer Linear Programming to solve the optimization problem, which is theoretically sound, and demonstrated empirically that this method outperforms local algorithms as well as a greedy optimization algorithm on the graph learning task.

the entailment hierarchy can be used as an additional facet, which can improve navigation and provide a compact hierarchical summary of the propositions.


Besides, when users post messages in the online forums, they are accustomed to be casual and use some synonymous words interchangeably in the posts, which is believed to be a significant situation in Chinese forums especially.

a specially designed model may learn semantic knowledge by reconstructing a great number of questions using the information in the corresponding answers.


Content selection is now the probability of a word appearing in the caption given the image and its associated document which we obtain from the output of our image annotation model (see Section 4).

like captions, headlines have to be catchy to attract the reader's attention.


The model summaries were created manually based on image descriptions taken from VirtualTourist and contain a minimum of 190 and a maximum of 210 words.

then, if length limit is not violated, we fill the summary with sentences from the "background" category until the word limit of 200 words is reached.


In other words, the coreference task as defined by MUC and ACE is geared toward only identifying coreference relations anchored to an entity within the text.

we utilised SVM rank8 as an implementation of the Ranking SVM algorithm, in which the parameter c was set as 1.0 and the remaining parameters were set to their defaults.


DH5 : yes, no a binary value indicating that P has never been referred to by any mentions in the preceding utterances.

in addition to the artificial nature of interaction, such as using keyboard input, this corpus only records restricted types of data.


In the NLP literature, model-based reinforcement learning techniques are commonly used for dialog management (Singh et al., 2002;Lemon and Konstas, 2009;Schatzmann and Young, 2009).

learning directly from annotations results in a low-quality environment model due to the relative lack of exploration,  Figure 4: Examples of automatically generated paraphrases for high-level instructions.


Our approach could be called lightly-supervised, since it does not require manual annotation of a single complete parse tree.

we reckoned editorialized content would lead to fewer extracted non-sentences.


For each corpus used in this paper, we use the tagging annotation guidelines to manually define a fixed mapping from the corpus tagset to the following coarse tagset: noun, verb, adjective, adverb, conjunction, preposition, determiner, interjection, numeral, and pronoun.

beyond the simplistic GLObAL phylogeny, the more nuanced LINGUIS-TIC model gave large improvements for English, Swedish, and Portuguese.


In these less-stable cases, there seemed to be an interaction between the variance and the choice between head conventions.

instead of parameterizing the objective in Section 2.4 in terms of multiple sets of weights, one at each node in the phylogeny (the hierarchical parameterization, described in Section 2.4), it is equivalent to parameterize this same objective in terms of a single set of weights on a larger of group features (the flat parameterization).


We also observed that the most common errors can be summarized succinctly by looking at attachment counts between coarse parts-of-speech.

because that feature does not show up in any other language, it is not usefully controlled by the prior.


One randomly maps each such word to a cluster, using a probability proportional to the number of unique known words already assigned to that cluster.

explicit modifications to these algorithms were introduced in order to bias their model to produce such a distribution (see (Clark, 2003;Johnson, 2007;Reichart et al., 2010b)).


For an object name Obj we query the Web using a small set of pre-defined co-hyponymy patterns like "as * and/or [Obj]" 2 .

some of the methods are suitable for retrieval of numerical attributes.


Hence a promising direction would be to use our approach in combination with Wikipedia data and with additional manually created attribute rich sources such as Web tables, to achieve the best possible performance and coverage.

in Question Answering (QA) scenarios we do not have such information.


We use the set of frequent words F to generalize words to "word classes".

the authors describe two soft matching models: one is based on an n-gram language model (with the Expectation Maximization algorithm used to estimate the model parameter), the other on Profile Hidden Markov Models (PHMM).


Again many of the dominant features are consistent with our intuition.

the first type of lexicon is an IH lexicon comprised of a list of attribute names for the intent class, e.g., "box office" and "review" for the intent class Movie.


So far, we have only considered a single assisting language.

(1999) use parallel blind relevance feedback, i.e.


We also observe that the best assisting language varies with the source language.

as shown in (Chinnakotla et al., 2010), having a better query translation system can only lead to better performance.


(iii) In a few rare cases inaccuracy in the back transla-  tion affects performance as well.

combining the evidence from all the feedback models of more than one assisting language, to get a feedback model which is better than that obtained using a single assisting language.


They divided the sentiment words into query-dependent and query-independent by utilizing several sentiment expansion techniques, and integrated them into a mixed model.

besides, in our model no training data is required.


Step 2: Include seg i+1 if seg i is included and seg i+1 contains one or more feature terms.

music RSs recommend individual tracks, not full albums, e.g.


The classic tf.idf formula assigns weight w i to term i in document D as: where tf i is the number of times term i occurs in D, idf i is the inverse document frequency of term i, N is the total number of documents and df i is the number of documents that contain term i.

the weight of term Table 2: SMART notation for inverse document frequency variants.


use a boosting-based algorithm to compute the usefulness of a subtree.

klenner (2007) and Finkel and Manning (2008) use ILP to ensure that the pairwise classification decisions satisfy transitivity.


This survey paper aims to complement, rather than supersede, these previously published materials.

for example, three NPs are likely to end up in the same cluster in the resulting partition even if there is strong evidence that A and C are not coreferent, as long as the other two pairs (i.e., (A,B) and (B,C)) are classified as positive.


An important issue with ranking models that we have eluded so far concerns the identification of non-anaphoric NPs.

overall performance on the coreference task might not improve.


This allows us to factorize the joint distribution of feature variables X conditioned on Z into product of marginals.

datasets One dataset we employed is the automatic content extraction (ACE) (ACE-Event, 2005).


We highlight the systematic preferences of each measure to reward different configurations.

the score improvement achieved by HEAD MATCH is much more noticeable on ACE than on OntoNotes, which indicates that many of its coreferent mentions share the same head.


In our mapping, possible alignment links have a precise interpretation that dictates what phrasal translation rules can be extracted from a sentence pair.

empirically, we observe a BLeU score improvement of 1.2 over the best unsupervised baseline and 0.8 over the block ITG supervised baseline (Papineni et al., 2002).


However, the second sentence with the same verb is not, because the subject is a non-animate abstract concept.

for maximum accuracy, we decided to use only those sentences all the three annotators agreed, resulting in a total of 568 sentences.


[13] John(A) hit(V) him(O) with a bat(I).

a verb may appear in different contexts and can have multiple    Evidence of a word w being an action in eHow is denoted as E(w) where variable t is the sum of individual action probability values in D w the set of documents from which the word w has been extracted as an action.


YAGO Graph: Graph constructed from the (instance, attribute) pairs obtained from the YAGO KB (Suchanek et al., 2007), with 142k nodes and 777k edges.

the label propagation method presented by Zhu et al.


The following lemma gives a condition under which the assignment is consistent (so contradictions do not occur in the parse tree): Lemma 1.

we acknowledge helpful comments by the anonymous reviewers.


(1989) devoted several pages to a discussion of how to "modularize" type codes, which typically achieves a smaller code in exchange for a larger-time operation than bitwise AND as the implementation of type unification.

if x is a type in the module whose bottom is y, and z has no module, then x z = y z unless y z = y in which case x z = x; so it only remains to compute joins within the tree.


It follows from the work of Markowsky (1980) that the construction of Aït-Kaci et al.


In this paper, we focused on English Word Sense Disambiguation.

finally, the results show that WordNet++ enables competitive performance also in a fine-grained domain setting.


We finally retain the link for the DFS if such score is above an empirically determined threshold.

to date, the real potential of knowledge-rich WSD systems has been shown only in the presence of either a large manually-developed extension of WordNet (Navigli and Velardi, 2005) or sophisticated WSD algorithms .


the task of wikification proposed by Mihalcea and Csomai (2007) and Milne and Witten (2008b)).

using a richer disambiguation context helps to better choose the most appropriate WordNet sense for a Wikipedia page.


This is because oracle selects the most frequent words which also have a high chance of getting selected when a random sampling is done.

just to prove that a random injection strategy does as good as an ideal strategy we assume the presence of an oracle which identifies the W D1D2 non−conf ormists .


There may be 2 main reasons for the loss: (1) SV3 is the tuning set in SM07, and we inherit the thresholds for similarity metrics from that study.

for example for the 157 adjective target test words in SV2AW, there was a single word alignment for 51 of the cases, losing any tagging for these words.


A major issue with trainable NLG systems is the lack of availability of domain-specific data.

a human evaluation over 202 dialogue acts does not show any difference in naturalness and informativeness between BaGEL's outputs and human utterances.


As the length of the semantic stack sequence is not known before decoding, the active learning selection criterion presented in (9) is biased towards longer utterances, which tend to have a lower probability.

a human evaluation shows that BaGEL can generate natural and informative utterances from unseen inputs in the information presentation domain.


an utterance providing information about the object under discussion (inform) or specifying that the request of the user cannot be met (reject).

1 with wordlevel models, a major advantage of phrase-based generation models is that they can model longrange dependencies and domain-specific idiomatic phrases with fewer parameters.


We then translate a SCRISP generation problem into a planning problem.

our work also stands in a tradition of NLG research that is based on AI planning.


Using these paraphrase lattices as inputs, we obtained significant gains in BLEU scores for IWSLT and Europarl datasets.

in Europarl dataset, the proposed method consistently get higher score than baselines.


Statistical significance in BLEU score differences is tested by paired bootstrap re-sampling (Koehn, 2004).

considering the huge number of classes may cause serious data sparseness problem and thereby degrade the classification accuracy, we approximate CBSM by a binary classification problem which can be solved by the maximum entropy (ME) approach (Berger et al., 1996) as follows: where υ ∈ {0, 1} is the indicator whether the source-side rule is applied during decoding, υ = 1 when the source-side rule is applied, otherwise υ = 0; h i is a feature function, λ i is the weight of h i .


Our method uses reordering graphs to represent all phrase segmentations of parallel sentence pairs, and then gets the fractional counts of bilingual phrases for orientations from reordering graphs in an inside-outside fashion.

the phrase-based reordering model (tillmann, 2004) determines the presence of the adjacent bilingual phrase located in position (s − 1, v + 1) and then treats the orientation of bp as S. Given no constraint on maximum phrase length, the hierarchical phrase reordering model (Galley and Manning, 2008) also analyzes the adjacent bilingual phrases for bp and identifies its orientation as S. given a bilingual phrase, the abovementioned models just consider the presence of an adjacent bilingual phrase rather than the number of adjacent bilingual phrases.


All the other three experiments were based on the IST-ITG method which makes use of syntac-tic constraints.


This comparison shows it is necessary to explicitly distinguish nodes in the source parse trees when they are used as reordering constraints.

17,240 nodes were labeled as frontier nodes and 5,736 nodes were labeled as interior nodes.


Using Ψ R n is almost twice as fast as the sequential method.


Since antecedents of NPs and pronouns (both salient NPs) often occur in similar syntactic environments, we use an alignment function for pronoun coreference as a starting point.

our model is actually a mixture of two IBM models, p C and p N , where p C produces NPs with antecedents and p N produces pairs that share a head, but are not coreferent.


This indicates that PCFG-I is contributing substantially to the performance of PCFG-E.

in our experiments, we used the Stanford Parser (Klein and Manning, 2003b;Klein and Manning, 2003a) and the OpenNLP sentence segmenter (http://opennlp.sourceforge.net/).


I am looking for a component", pointing out to the student the kind of information it is looking for.

there was no significant difference in learning gain between conditions.


The frequencies of other error types did not significantly differ between conditions.

the goal of the help messages is to give the student as much information as possible as to why the system failed to understand them but without giving away the answer.


We describe an evaluation of an error recovery policy in the BEETLE II tutorial dialogue system and discuss how different types of interpretation problems affect learning gain and user satisfaction.

this provides a measure of robustness, and previous evaluations of ASR in spoken tutorial dialogue systems indicate that neither word error rate nor concept error rate in such systems affect learning gain (Litman and Forbes-Riley, 2005;Pon-Barry et al., 2004).


The content of the bottom out is the same as in the baseline, except that the full system indicates clearly that the answer was incorrect or was not understood, e.g., "Not quite.

the problems arising from student use of non-standard terminology appear to have negative consequences.


Recent research aims to develop methods that produce detailed analyses of student input, including correct, incorrect and missing parts (Nielsen et al., 2008;Dzikovska et al., 2008), because the more detailed assessments can help tailor tutoring to the needs of individual students.

it was easier to design an effective prompt.


Along with museum descriptions, studied by Cheng, it seems that news and narrative are genres where authors naturally use a large number of descriptive referring expressions.

we make two contributions in this paper.


Note that at- tributes are primed, not values; a participant may have heard front facing in the prime turn, while the target has a different value for this attribute (cf.

as far as we know, experimental evidence for alignment on the level of content planning has never been given, and neither have alignment effects in modifier orderings during realization been shown.


This type of precise, formalized hypothesis testing is only possible if standardized data sets and uniform evaluation procedures are available, as outlined in the present paper.

most surprisingly, they achieve this competency on the basis of limited training data (Hart and Risley, 1995), using learning algorithms that are largely unsupervised.


Another key property of human language processing is the fact that it operates with limited memory, and that structures in memory are subject to decay and interference.

there is considerable experimental evi-Model Property Evidence Rank Surp Pred Stack Efficiency and robustness Ferreira et al.


The methods cannot correct syntactic annotation errors, because syntactic annotation is structural.

the methods cannot correct errors in structural annotation.


In statistical MT systems, automatic evaluation of translations is essential for parameter optimization and system development.

manual evaluation is important in the comparison of different MT systems and for the validation and development of automatic MT evaluation measures, which try to model human assessments of translations as closely as possible.


Figure 3 documents the issue across languages: the lower the BLEU score itself (i.e.

a weighted linear combination of SemPOS and BLEU (computed on the surface representation of the sentence) should compensate for this.


Also, they can be used directly for testing paraphrase applicability (Szpektor et al., 2008), a task that has recently become prominent in the context of textual entailment (Bar-Haim et al., 2007).

4 We did not have access to the TDP09 predictions to do significance testing.


For learning of the model, we borrow a fundamental idea of Kazama and Torisawa's perceptron learning algorithm.

in terms of sense disambiguation results, incorporating F P A and F G worked well.


To partially resolve this problem, we propose semantics-driven shallow parsing, which takes into account both syntactic structures and predicate-argument structures.

e.g., NPˆS implicitly represents Subject while NPˆVP represents Object.


Yet another direction of this research is to investigate if our methodology is applicable to other types of collocations, such as AN and PN in addition to VN dealt with in this paper.

at run-time, we predict these collocations for S as suggestions.


We can observe that the desired collocation "introduce method", conforming to "VERB+NOUN" relationship, exists within the sentence.

the results indicate that on average users could easily find answers (exactly reproduction of the blanked out collocates) in the first two to three ranking of suggestions.


We extracted 364 collocations among 600 randomly selected sentences as the held out test data not overlapping with the training set.

this extraction task can be complicated.


The application of original HAL to query expansion attempted to incorporate statistical word association information, but did not take into account the syntactical dependencies and had a high processing cost.

our work is in line with Pado and Lapata (2007) in constructing a semantic space with syntactic information, but builds our space from events, states and attributions as defined linguistically by Bach (1986).


As the next step, we will try to exploit bilingual knowledge to improve the monolingual parser, i.e.

this may be caused by the data sparseness problem, which results in inaccurate parameter estimation of the new feature.


Hierarchical rules are more powerful than conventional phrases since they have better generalization capability and could capture long distance reordering.

the gain is +2.21% BLEU on test04, and +1.33% on test05.


The new pair (z , t ) is accepted with the probability In order to implement the MH algorithm for our model, we need to define the set of potential moves (i.e.

similarly, the query-focused summarization (e.g., (Daumé and Marcu, 2006)) is also related but it focuses on sentence extraction rather than on joint segmentation.


For example, a feature might denote whether the two NPs agree in number, or whether they have any words in common.

the authors would like to thank the anonymous reviewers for their useful comments.


Coreference resolution is a complex problem, and successful systems must tackle a variety of non-trivial subproblems that are central to the coreference taske.g., mention/markable detection, anaphor identification -and that require substantial implementation efforts.

we hope that Reconcile will support experimental research in coreference resolution and provide a state-of-the-art coreference resolver for both researchers and application developers.


The performance for the location type argument improved drastically.

we thank Kevin Duh for his valuable comments.


To confirm this, we performed an experiment in which we gave the rules of the baseline system to our system as initial rules and subsequently performed our incremental learning.

in PAS tasks, we need to check the exclusiveness of the argument types (for example, a predicate argument structure does not have two nominative ar-guments), and we cannot directly use the method.


For more information, readers can refer to relevant papers and our source codes 2 that are well commented.

rank Feature rank Feature 1  Table 4 shows the ten most useful features in SrC.


Head position suggests that boundary words are good approximation of head word features.

this problem in Chinese is much more serious due to the state-of-the-art of Chinese parsing.


These methods require sentencealigned parallel data, which can be costly to obtain and difficult to scale to many languages.

3 Proposed Method Let X = {X l } l∈L be a non-parallel and nonannotated multilingual corpus, where X l is a set of sentences in language l, and L is a set of languages.


The rule probabilities for each nonterminal A of PCFG G l in language l consist of: 1) θ Al = {θ lAt } t∈{0,1} , where θ lA0 and θ lA1 represent probabilities of choosing the emission rule and the binary production rule, respectively, 2) φ lA = {φ lABC } B,C∈K , where φ lABC represents the probability of nonterminal production A → BC, and 3) ψ lA = {ψ lAw } w∈W l , where ψ lAw represents the probability of terminal emission A → w. , and ψ lAw ≥ 0.

our method does not require any such prior information.


Figure 1 shows a tournament for the example of "with," where the word "watched" finally wins.

although the proposed model's worst-case time complexity is O(n 2), the experimental results demonstrated an average parsing time not much slower than O(n).


The dependency relation between the target trees is represented by the three words w i , MPHC, and w j .

the proposed tree-based model is deterministic but takes a step towards global optimization by widening the search space to include all necessary words connected by previously judged head-dependent relations, thus achieving a higher accuracy yet largely retaining the speed of deterministic parsing.


This enhancement from words to trees allows removal of both the Reduce transition and certain preconditions.

the features are designed to incorporate these words, their relational words, and the three words next to w j .


Tri-training, explained below, is a semi-supervised learning method which requires large amounts of data.

of course this is not a standard self-training set-up, but self-training informed by unsupervised word clusters.


Table 3 shows that our method produces much smaller models than EM, and produces Viterbi taggings with many fewer tag-bigram types.

in the latter approach, because there is no single objective function to optimize, it is not entirely clear how to generalize this technique to other problems.


For all four selection methods, plots of test set perplexity vs. the number of training data tokens selected are displayed in Figure 1.

our method is a fairly simple variant of scoring by perplexity according to an in-domain language model.


Parsing was performed using a Metropolis-Hastings sampler to draw derivation samples for a string, from which the best tree was recovered.

in recent work we proposed a TSG model which infers an optimal decomposition under a nonparametric Bayesian prior .


This observation indicates that we should be able to improve the answer accuracy of the QA system by optimizing the LL of the filter model in isolation, similar to how, in automatic speech recognition, the LL of the language model can be optimized in isolation to improve the speech recognition accuracy (Huang et al., 2001).

in some cases both who and where q-a pairs occurred in the same cluster, so as to better answer questions like Who won the World Cup?, where the answer could be a country name.


The complete system output and annotations are publicly available at: http://www.cs.cornell.edu/˜cristian/acl2010/.

an example is the word 'bans': 'The law bans opium use' ⇒ (⇐) 'The law bans narcotic use'.


DLD09 use NPIs as signals of the occurrence of downward-entailing operators.

he shows that English is one of the minority of languages (11 out of 40) 9 in which there exists an indefinite pronoun series that occurs in all (haspelmath's) classes of DE contexts, and thus can constitute a sufficient seed on its own.


Finally, we noted some cross-linguistic differences in performance, and found an interesting connection between these differences and Haspelmath's [9] characterization of cross-linguistic variation in the occurrence of indefinite pronouns.

with a small amount of effort, one can form a minimal NPI seed set for the DLD09 method by using an appropriate target-language translation of 'any'.


(See blue/dark bars in figure 3 in the externallyavailable appendices for detailed results.)

for most languages, we lack the critical clues that DLD09 depends on.


We contrast it with text categorization and with opinion classification by employing models routinely used for such tasks.

among the many proposed remedies (Baayen, 2001;Jansche, 2003;Baroni and Evert, 2007;Bhat and Sproat, 2009), we would like to draw attention to the following insight articulated most clearly in Jansche (2003).


Also following the setting in (Wan, 2009), we only use the Chinese unlabeled data and English training sets for our SCL training procedures.

this modification will not affect the deduction procedure and results in (Ando and Zhang, 2005).


In the original SCL as we mentioned earlier, the final classifier is trained on the extended features (x, w * x).

in table 1, we give some of the pivot features with English English Pivot Features "poor quality", "not buy", "easy use", "very easy" "excellent", "perfect", "still very", "garbage", "poor", "not work", "not to", "very comfortable" Chinese Pivot Features wanmei(perfect), xiaoguo hen(effect is very...) tisheng(improve),feichang hao(very good), cha(poor), shushi(comfortable), chuse(excellent)  Data sparseness is a common problem in NLP tasks.


So, now we will have (x s , x tp ) and (x sp , x t ) where x (s|t)p are pivot features in the source and the target languages.

while machine translation has been the subject of a great deal of development in recent years, many of the recent gains in performance manifest as syntactically as opposed to semantically correct sentences.


Up to A k , we get a list of candidates T : T 1 , T 2 , ..., T L , ranked by probabilities P k (T |S) in descending order.

3 Joint optimization and its fast decoding algorithm We denote a word in the source language by S, a segmentation of S by A, and a word in the target langauge by T .


Generally, the LOC and PER classes benefitted more from the head word features, SHW), than the other classes.

gazetteers: automatically harvested and manually cleaned Person NE class (PER), geopolitical Entity NE class (gPE), and Organization NE class (ORg) lexica; 4.


The sequence with name "President of the United States in 1960" will not satisfy density, since it will have extractions for only one position.

open IE systems are domainindependent, but extract "flat" sets of assertions that are not organized into frames and slots (Sekine, 2006;Banko et al., 2007).


Recent work (Bui et al., 2009) showed that Directed Graphical Models (DGMs) outperform other machine learning techniques such as Support Vector Machines (SVMs) for detecting individual DDAs.

rP utterances propose this resolution (e.g.


http://www.graphpad.com/quickcalcs/ttest1.cfm the DGMs studied in (Bui et al., 2009).

dynamic Bayesian Networks (dBNs) are a popular model for probabilistic sequence modeling because they exploit structure in the problem to compactly represent distributions over multi-state and observation variables.


For both errors and duration, we did not find any interaction effects with Driving Conditions.

in computing the number of "correct" answers, for each SMS reply, we counted an an-swer to be correct if it was included among the participants' set of semantically sufficient 4-best list items.


The best results on most of our data were obtained using Hidden Naive Bayes (HNB) (Zhang et al., 2005).

the results, which partly confirm those obtained on a smaller dataset in Paggio and Navarretta (2010), must be seen in light of the fact that our gesture annotation scheme comprises more fine-grained categories than most of the studies mentioned earlier for both head movements and face expressions.


Due to the small sample size, we cautiously infer that last sentences express polarity better than second sentences, bearing in mind that the second sentence in our experiment represents any other sentence in the text except for the first one.

thirty-six subjects participated in the second experiment: 23 women and 13 men ranging in age from 20 to 59.


When dealing with polarity-classification (as with topic-extraction), one should again identify the most uninformative yet dominant proposition of the text.

they first focused on the last sentence of posts as representing the polarity of the entire posts.


The profiles will be used as a feature in our computational analysis.

we first briefly review the well-established structure of text units while comparing notions of topicextraction vs. our notion of polarityclassification.


The overall POS result for run 3 is slightly less than run 2.

for each source token TEXT, the features include the ones arising from the named stems of all the regexes that text-match that TEXT, as shown in column 4, and the gold label is the appropriate stem name together with the POS tag, as shown in column 3.


To date, the conventional wisdom in the error detection community has been to avoid the use of statistical parsers under the belief that a WSJtrained parser's performance would degrade too much on noisy learner texts and that the traditionally hard problem of prepositional phrase attachment would be even harder when parsing ESL writing.

for our immediate future work, we plan to carry out the ESL evaluation on a larger test set to better gauge the usefulness of a parser in this context, to carry out a detailed error analysis to understand why certain parse features are effective and to explore a larger set of features.


In a similar way, a centroid is also produced for each candidate (or unlabeled) entity.


Word-Alignment is a particularly challenging problem and has been addressed in a completely unsupervised manner thus far (Brown et al., 1993).

the restriction of the alignment paths reduces to restricting the summation in EM.


In this case, we treat the set of test instances (without their gold labels) as the unlabeled data.

dimensionality of the target space was set at d = log n 2 log 1 , as prescribed in (Bingham and Mannila, 2001).


The 2nd column, LSA extract , corresponds to the performance of our system at producing summaries by sentence extraction only.

within the 100word window imposed by the TAC guidelines, our system is able to fit more (and hence shorter) sentences as we decrease the percentage cutoff.


Our feature space is thus the Euclidean vector space R 7N +8 .

we also tried K-means variants in which the Euclidean distance metric is replaced by the city block distance L 1 and the cosine dissimilarity, respectively, with slightly worse outcomes.


To distinguish the two, we refer to (5) as decipherment, rather than decoding.

during decipherment, our goal is to estimate the channel model parameters θ. Re-writing Equations 3 and 4 for word substitution decipherment, we get: Challenges: Unlike letter substitution ciphers (having only 26 plaintext letters), here we have to deal with large-scale vocabularies (10k-1M word types) and corpora sizes (100k cipher tokens).


Alike English, the extra meaning provided by a Japanese auxiliary verb alters the basic meaning of the main verb so that the main verb has one or more of the following functions: passive voice, progressive aspect, perfect aspect, modality, dummy, or emphasis.

as such, an HPSG parse forest can be considered to be a forest of signs.


In this section, we first describe an algorithm that attaches function words to a packed forest guided by target chunk information.

this is the motivation of the present approach of re-aligning the target function words to source tree fragments, so that the influence of incorrect alignments is reduced and the function words can be generated by tree fragments on the fly.


However, the CRF-LM output is more grammatical than the baseline, because not only do the adjective and noun agree for case, but the noun 'baskien' to which the postposition 'kanssa' belongs is marked with the correct genitive case.

a typical approach is to select a highly inflecting class of words and segment them for particular morphology (de Gispert and Mariño, 2008;Ramanathan et al., 2009).


c. CRF-LM: perusoikeuskirja on hyväksytty tänään , sekä vahvistaa ja antaa (antaa/VERB/ACT/INF/SG/ LAT-give) konkreettisen (konkreettinen/ADJECTIVE/SG/GEN,ACC-concrete) muodon (muoto/NOUN/SG/GEN,ACCshape) yhteisiä perusoikeuksia ja perusarvoja , yhteisön on perustuttava.

we expect this approach to work for other agglutinative languages as well.


3, all three translations correspond to the English text, 'with the basque nationalists.'

in phrases used while decoding the development and test data, roughly a quarter of the phrases that generated the translated output included segmentations, but of these, only a small fraction (6%) had a hanging morpheme; and while there are many possible reasons to account for this we were unable to find a single convincing cause.


Following this assumption, the authors propose a measure to estimate the amount of annotation noise in the data after removing all hard cases.

a smaller set of data points is able to boost classifier performance and to yield an accuracy comparable to the one obtained when training the same system on a larger set of randomly chosen data.


3 An alternative is to use the Jensen-Shannon (JS) divergence, however, our preliminary experiments seem to suggest that the symmetrized KL divergence is preferable.

we follow (McCallum et al., 2006) and use a multi-conditional objective, a specific form of hybrid learning, to emphasize the importance of labels y: where α is a weight, α > 1.


We assume similar definitions for p * v (v 1 , v 2 ) and score * v (v 1 , v 2 ).

deriving the simple algorithm will be useful in developing intuition, and will lead directly to the algorithm for the unrestricted case.


Unlike global models, local models leverage relationships between documents in a corpus.

our best performing model, which interpolates between a foreground click model and a smoothed background model, significantly reduces testing error when compared against a strong baseline, by 18%.


It also shows that S 3 H (with FCC) can obtain the best precision for all bits.

the similarity search in the Hamming space 1 is much more efficient than in the original attribute space (Manku et al., 2007).


The published work on the more advanced methods usually requires access to large amounts of proprietary user data such as query logs and clicks (Barr et al., 2008;Guo et al., 2008;Guo et al., 2009).

instead, we are interested in annotation of queries of different types, including verbose natural language queries.


Some queries are keyword concatenations, some are semicomplete verbal phrases and some are wh-questions.

given a sentence from a corpus that is relevant to the query such as "Hawaiian Falls is a family-friendly waterpark", the word "falls" is correctly identified by a standard POS tagger as a proper noun.


Note that it's not meaningful to directly compare absolute weight values between query-aggr and query-comp because source query weights from distinct weighting methods have different range and scale.

although instance weighting scheme may be applied to documents for ranking model adaptation, the difference between classification and learning to rank should be highlighted to take careful consideration.


For pairwise algorithms, a straightforward way is to assign the query weight to all the document pairs associated with this query.

in this case, a small number of documents that were weighted inaccurately can make significant impact on many number of document pairs.


Adaptation experiments on LETOR3.0 data set demonstrate that query weighting significantly outperforms document instance weighting methods.

when TD serves as the source domain, it leads to the problem that too many document pairs were generated for training the RSVM model.


After selecting salient features, the SCL algorithm is used to train a binary classifier.

in addition to word-level sentiment features, we replace words with their POS tags to create POS-level sentiment features.


Alternatively, one could use the ranking score, score(v r d , d), itself as the value of the appended base entries.

table 1 shows two positive and one negative review from each domain.


However, not all words can be cleanly classified into domain specific or domain independent, and this process is conducted prior to training a classifier.

my husband loves the burgers that I make from this grill.


Next, for each lexical element we measure its relatedness to other lexical elements and group related lexical elements to create a thesaurus.

a model that is trained only using book reviews might not have any weights learnt for delicious or rust, which would make it difficult for this model to accurately classify reviews of kitchen appliances.


In the experiments, we estimate them on a tweet corpus containing 20 million tweets.

then predefined rules are matched in the context to determine the sentiment about the target.


As illustrated in Figure 1, each circle in the graph indicates a tweet.

in recent years, sentiment analysis (SA) has become a hot topic in the NLP research community.


According to the results, adding all extended targets improves the accuracy by about 1 point.

based on these three kinds of relations, we can construct a graph using the input tweet collection of a given target.


(2003) constructed a database of 13,000 English idioms tagged with syntactic structures; Villavicencio (2004) attempted to compile lexicons of English idioms and verb-particle constructions (VPCs) by augmenting existing single-word dictionaries with specific tables; Baptista et al.


The conventional idiom dictionaries published for human readers have been occasionally used for the evaluation of automatic extraction methods in some past studies.

3 For example, an idiom 真っ赤-な -嘘 makka-na-uso (lit.


The optimisation problem is equivalent to that for the classification model on pair-wise difference vectors.

this suggests that there is room for improvement in the language models we developed to estimate the error-rate.


For the experiments reported here, we use complexity measures representing the sum of the longest distance in word tokens between a head and dependent in a grammatical relation (GR) from the RASP GR output, calculated for each GR graph from the top 10 parses per sentence.

the addition of an incoherence metric to the feature set of an AA system has been shown to improve performance significantly (Miltsakaki and Kukich, 2000;Miltsakaki and Kukich, 2004).


By using a different loss function, the ε-insensitive loss function (Smola, 1996), SVMs can also perform regression.

although our corpus of manually-marked texts was produced by learners of English in response to prompts eliciting free-text answers, the marking criteria are primarily based on the accurate use of a range of different linguistic constructions.


Since we had hundreds of submissions to base our decisions on, it was fairly easy to identify the cheaters and people with poor English skills 3 .

the qualification process was done manually by the authors.


Machine paraphrasing has many applications for natural language processing tasks, including machine translation (MT), MT evaluation, summary evaluation, question answering, and natural language generation.

to evaluate the results quantitatively, we used the BLEU/PINC metric.


Without these resources, researchers have resorted to developing their own small, ad hoc datasets (Barzilay and McKeown, 2001;Shinyama et al., 2002;Barzilay and Lee, 2003;, and have often relied on human judgments to evaluate their results (Barzilay and McKeown, 2001;Ibrahim et al., 2003;Bannard and Callison-Burch, 2005).

the data thus has some similarities to parallel news descriptions of the same event, while avoiding much of the noise inherent in news.


"), their opinion of the event shown, or the moral of the story.

pEM had the worst correlation with human judgments of all the metrics.


Since the scope of this data collection effort extended beyond gathering English data alone, we • Someone is coa+ng a pork chop in a glass bowl of flour.

our approach only requires monolingual data, and evaluation can be performed using arbitrarily small, highly-parallel datasets.


(2006) have derived a generalized inside-outside algorithm by applying the standard EM algorithm.

its decoder uses a trigram language model trained with modified Kneser-Ney smoothing (Kneser and Ney, 1995) on a 200 million tokens corpus.


All these data sets are taken from the LDC English Gigaword corpus with non-verbalized punctuation and we remove all punctuation.

eM update: Perform one iteration (or several iterations) of eM algorithm to estimate model parameters that maximizes N -best-list likelihood of the training corpus D, (a) e-step: Compute the auxiliary function of the N -best-list likelihood with respect to p ′ to get new update for p. Iterate steps (1) and (2) until the convergence of the N -best-list likelihood.


The semantic frame structures thus obtained for the MT output are compared to those in the reference translations, frame by frame, argument by argument.

instead, we aim for MT evaluation metrics that provide fine-grained scores in a way that also directly reflects interpretable insights on the strengths and weaknesses of MT systems rather than simply replicating human assessments.


Therefore, they all understand the translation almost the same way, even if the translation is incorrect.

we study the cost/benefit trade-off of using human annotators from different language backgrounds for the proposed evaluation metric, and compare whether providing the original source text helps.


The forms produced by the tool from the lemma of an observed inflected word form were subjected to several restrictions: • For nouns, generated forms had to match the original form for number.

parameter estimation was performed using a modified version of the maximum entropy module from Scipy (Jones et al., 2001) and the LBFGS-B algorithm (Byrd et al., 1995).


The standard relative frequency estimates for the p(X|Y ) phrase model and the lexical models do not provide reasonable values for the decoder scores for unobserved rules and words.

the inflection for number is particularly easy to model in translating from English, since it is generally marked on the source side, and POS taggers based on the Penn treebank tag set attempt to infer it in cases where it is not.


A span chart has the form [i 1 , j 1 ; µ 1 ], where µ 1 is a set of messages, and its items are likewise prioritized by a partial score s 1 .

crucially for the task of modeling morphological generalizations, it estimates feature parameters from the entire training set rather than as a collection of separate classifiers.


The highest scores were achieved by the full exponential model, although baseline B produced surprisingly disparate effects for the two data sets.

birch et al (2008) find that translation quality achieved by a popular phrase-based system correlates significantly with a measure of targetside, but not source-side morphological complexity.


Figure 1 (top) shows a portion of the ciphertext along with the original plaintext used to create the cipher.

data: Figure 1 (bottom) displays the Zodiac-408 cipher (consisting of 408 tokens, 54 symbol types) along with the original plaintext message.


Probabilistic Decipherment: Our decipherment method follows a noisy-channel approach.

his method relies on using plaintext words from the known solution to solve the cipher, which departs from a strict decipherment scenario.


To verify the usefulness of our method's predicate labeling, we perform a final set of experiments where predicate labels are selected uniformly at random within our full model.

at each step, we perform 500 Monte-Carlo roll-outs.


Since our model is a non-linear approximation of the underlying action-value function of the game, we learn model parameters by applying non-linear regression to the observed final utilities from the simulated roll-outs.

the success of Monte-Carlo search is based on its ability to make a fast, local estimate of the ac-procedure PlayGame () Initialize game state to fixed starting state Compute average observed utility for each action Sample action from action-value function in -greedy fashion: Execute selected action in game: if game is won or lost break end Update parameters w of Q(s, a) Return action and observed utility: Algorithm 1: the general Monte-Carlo algorithm.


The parameters w of Q(s, a) are learned based on feedback from the roll-out simulations.

since our model is a non-linear approximation of the underlying action-value function of the game, we learn model parameters by applying non-linear regression to the observed final utilities from the simulated roll-outs.


Datasets We use the official game manual for Civilization II as our strategy guide.

in the second layer of the neural network, the units z represent a predicate labeling e i of every sentence y i ∈ d. our intention is to incorporate, into action-value function Q, information from only the most relevant sentence.


The compositional semantics of a wildcard combination can thus be understood in set-theoretic terms.

it seems ?P does a rather good job of capturing the feel of P. Creative information retrieval is not a single application, but a paradigm that allows us to conceive of many different kinds of application for creatively manipulating text.


The worse performance was obtained when k = 2 local histograms are considered (see row 3 in Table 2).

this result evidences that authors use similar word/character distributions at similar locations when writing different documents.


LOWBOW representations have also been applied to discourse segmentation (AMIDA, 2007) and have been suggested for text summarization (Das and Martins, 2007).

on average, the worse kernel was that based on the earth mover's distance (EMD), suggesting that the comparison of local histograms at different locations is not a fruitful approach (recall that this is the only kernel that compares local histograms at different locations).


The Earth mover's distance (EMD), on the other hand, is an estimate of the optimal cost in taking local histograms from Q to local histograms in P (Rubner et al., 2001); that is, this measure computes the optimal matching distance between local histograms from different authors that are not necessarily computed at similar locations.

sequential information of the document is considered throughout these local histograms.


In order to compare our methods to related works we adopted the following experimental setting.

unlike usual text categorization tasks, where the core problem is modeling the thematic content of documents (Sebastiani, 2002), the goal in AA is modeling authors' writing style (Stamatatos, 2009b).


Additionally, we make several theoretical contributions based on an examination of the feature weights learned by our machine learning classifiers.

if human performance is low, then our deceptive opinions are convincing, and therefore, deserving of further attention.


Unfortunately, we found that some Turkers selected among the choices seemingly at random, presumably to maximize their hourly earnings by obviating the need to read the review.

while these studies compare n-gram-based deception classifiers to a random guess baseline of 50%, we additionally evaluate and compare two other computational approaches (described in Section 4), as well as the performance of human judges (described in Section 3.3).


Furthermore, most previous work in the area has focused on the detection of DISRUPTIVE OPINION SPAM-uncontroversial instances of spam that are easily identified by a human reader, e.g., advertisements, questions, and other irrelevant or nonopinion text (Jindal and Liu, 2008).

for each of the 20 chosen hotels, we select 20 truthful reviews from a log-normal (lefttruncated at 150 characters) distribution fit to the lengths of the deceptive reviews.


With the ever-increasing popularity of review websites that feature user-generated opinions (e.g., TripAdvisor 1 and Yelp 2 ), there comes an increasing potential for monetary gain through opinion spaminappropriate or fraudulent reviews.

lIWC counts and groups the number of instances of nearly 4,500 keywords into 80 psychologically meaningful dimensions.